In an earlier post we introduced the idea of investing in hedge fund strategies,
and thereby picking up the premium income that arises from exposure to the
Dynamic Trading Risk Factor — which he hypothesize is the true
reason why hedge funds outperform the general market — by
investing in the publicly traded companies who's monthly returns are
strongly influenced by the returns of that factor.
We used standard linear regression analysis to identify a
small portfolio of the five stocks who's monthly total returns are best
described by this factor. To select these members, we studied the current
constituency of the S&P 500 Financials Select Sector
Sub-Index, represented by the
XLF exchange traded fund. As this universe selection imposes a style
and survivorship bias on our universe, we need to exercise caution in
our use of the data.
The chart below illustrates an attempt to peer into this data. Following the cross
validation idea of comparing analysis on randomly selected sub-samples
of the data, we took the current members of
the XLF and, for each member, took a randomly chosen sample of 50% of
our data, and it's complement, for regression of the member's montly returns
onto our series of the dynamic
trading risk factor. Each member's data had a freshly chosen
set of random sub-samples, which should remove systematic correlations on a
stock-by-stock basis. The data plotted is the signed square root of the
sub-sample regression R² for each stock analyzed.
We see that, on average, there does seem to be meaningful similarity of the effectiveness of the
risk factor between the two random sub-samples.